Overview

Dataset statistics

Number of variables16
Number of observations5810
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory198.1 B

Variable types

Text1
Numeric14
Categorical1

Alerts

danceability is highly overall correlated with valenceHigh correlation
energy is highly overall correlated with loudness and 1 other fieldsHigh correlation
valence is highly overall correlated with danceabilityHigh correlation
loudness is highly overall correlated with energy and 2 other fieldsHigh correlation
acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
popularity is highly overall correlated with loudnessHigh correlation
artist_name has unique valuesUnique
artist_id has unique valuesUnique
key has 663 (11.4%) zerosZeros
instrumentalness has 479 (8.2%) zerosZeros

Reproduction

Analysis started2023-11-17 00:58:01.242808
Analysis finished2023-11-17 00:58:32.721633
Duration31.48 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

artist_name
Text

UNIQUE 

Distinct5810
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size443.3 KiB
2023-11-17T00:58:32.974135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length46
Median length37
Mean length12.15611
Min length1

Characters and Unicode

Total characters70627
Distinct characters101
Distinct categories17 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5810 ?
Unique (%)100.0%

Sample

1st rowFrank Sinatra
2nd rowVladimir Horowitz
3rd rowJohnny Cash
4th rowBillie Holiday
5th rowBob Dylan
ValueCountFrequency (%)
the 710
 
6.0%
136
 
1.1%
of 67
 
0.6%
john 61
 
0.5%
band 42
 
0.4%
johnny 37
 
0.3%
joe 36
 
0.3%
paul 35
 
0.3%
james 35
 
0.3%
david 32
 
0.3%
Other values (6549) 10675
90.0%
2023-11-17T00:58:33.454691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6873
 
9.7%
6056
 
8.6%
a 5269
 
7.5%
n 4289
 
6.1%
r 4195
 
5.9%
o 4092
 
5.8%
i 4027
 
5.7%
l 3194
 
4.5%
s 3010
 
4.3%
t 2833
 
4.0%
Other values (91) 26789
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 51777
73.3%
Uppercase Letter 11997
 
17.0%
Space Separator 6056
 
8.6%
Other Punctuation 471
 
0.7%
Decimal Number 132
 
0.2%
Dash Punctuation 62
 
0.1%
Other Symbol 50
 
0.1%
Other Number 27
 
< 0.1%
Format 21
 
< 0.1%
Currency Symbol 10
 
< 0.1%
Other values (7) 24
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1161
 
9.7%
B 1015
 
8.5%
S 996
 
8.3%
C 901
 
7.5%
M 900
 
7.5%
J 672
 
5.6%
R 612
 
5.1%
D 611
 
5.1%
L 579
 
4.8%
A 567
 
4.7%
Other values (17) 3983
33.2%
Lowercase Letter
ValueCountFrequency (%)
e 6873
13.3%
a 5269
10.2%
n 4289
 
8.3%
r 4195
 
8.1%
o 4092
 
7.9%
i 4027
 
7.8%
l 3194
 
6.2%
s 3010
 
5.8%
t 2833
 
5.5%
h 2295
 
4.4%
Other values (16) 11700
22.6%
Other Punctuation
ValueCountFrequency (%)
. 153
32.5%
& 134
28.5%
' 67
14.2%
" 38
 
8.1%
¡ 19
 
4.0%
, 19
 
4.0%
! 16
 
3.4%
/ 6
 
1.3%
6
 
1.3%
* 5
 
1.1%
Other values (5) 8
 
1.7%
Decimal Number
ValueCountFrequency (%)
0 22
16.7%
1 21
15.9%
2 19
14.4%
5 15
11.4%
3 13
9.8%
4 12
9.1%
7 10
7.6%
9 8
 
6.1%
8 6
 
4.5%
6 6
 
4.5%
Currency Symbol
ValueCountFrequency (%)
£ 5
50.0%
¢ 2
 
20.0%
¤ 2
 
20.0%
$ 1
 
10.0%
Modifier Symbol
ValueCountFrequency (%)
¸ 3
37.5%
´ 2
25.0%
¯ 2
25.0%
˜ 1
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 61
98.4%
1
 
1.6%
Other Number
ValueCountFrequency (%)
³ 23
85.2%
¼ 4
 
14.8%
Math Symbol
ValueCountFrequency (%)
± 4
80.0%
+ 1
 
20.0%
Initial Punctuation
ValueCountFrequency (%)
« 2
66.7%
1
33.3%
Space Separator
ValueCountFrequency (%)
6056
100.0%
Other Symbol
ValueCountFrequency (%)
© 50
100.0%
Format
ValueCountFrequency (%)
­ 21
100.0%
Other Letter
ValueCountFrequency (%)
º 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Control
ValueCountFrequency (%)
 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 63777
90.3%
Common 6850
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6873
 
10.8%
a 5269
 
8.3%
n 4289
 
6.7%
r 4195
 
6.6%
o 4092
 
6.4%
i 4027
 
6.3%
l 3194
 
5.0%
s 3010
 
4.7%
t 2833
 
4.4%
h 2295
 
3.6%
Other values (44) 23700
37.2%
Common
ValueCountFrequency (%)
6056
88.4%
. 153
 
2.2%
& 134
 
2.0%
' 67
 
1.0%
- 61
 
0.9%
© 50
 
0.7%
" 38
 
0.6%
³ 23
 
0.3%
0 22
 
0.3%
­ 21
 
0.3%
Other values (37) 225
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70315
99.6%
None 307
 
0.4%
Punctuation 4
 
< 0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6873
 
9.8%
6056
 
8.6%
a 5269
 
7.5%
n 4289
 
6.1%
r 4195
 
6.0%
o 4092
 
5.8%
i 4027
 
5.7%
l 3194
 
4.5%
s 3010
 
4.3%
t 2833
 
4.0%
Other values (68) 26477
37.7%
None
ValueCountFrequency (%)
à 156
50.8%
© 50
 
16.3%
³ 23
 
7.5%
­ 21
 
6.8%
¡ 19
 
6.2%
6
 
2.0%
£ 5
 
1.6%
± 4
 
1.3%
¼ 4
 
1.3%
¸ 3
 
1.0%
Other values (9) 16
 
5.2%
Punctuation
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%
Modifier Letters
ValueCountFrequency (%)
˜ 1
100.0%

artist_id
Real number (ℝ)

UNIQUE 

Distinct5810
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean583166.45
Minimum74
Maximum3670556
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.8 KiB
2023-11-17T00:58:33.619561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum74
5-th percentile31727.6
Q1169016.75
median423181
Q3782355.5
95-th percentile2126346.1
Maximum3670556
Range3670482
Interquartile range (IQR)613338.75

Descriptive statistics

Standard deviation647726.69
Coefficient of variation (CV)1.1107064
Kurtosis7.6930145
Mean583166.45
Median Absolute Deviation (MAD)300406.5
Skewness2.6155375
Sum3.3881971 × 109
Variance4.1954987 × 1011
MonotonicityNot monotonic
2023-11-17T00:58:33.758795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
792507 1
 
< 0.1%
504169 1
 
< 0.1%
476744 1
 
< 0.1%
392925 1
 
< 0.1%
391519 1
 
< 0.1%
385766 1
 
< 0.1%
347639 1
 
< 0.1%
329313 1
 
< 0.1%
320470 1
 
< 0.1%
320442 1
 
< 0.1%
Other values (5800) 5800
99.8%
ValueCountFrequency (%)
74 1
< 0.1%
335 1
< 0.1%
441 1
< 0.1%
589 1
< 0.1%
1097 1
< 0.1%
1098 1
< 0.1%
1113 1
< 0.1%
1163 1
< 0.1%
1190 1
< 0.1%
1266 1
< 0.1%
ValueCountFrequency (%)
3670556 1
< 0.1%
3661738 1
< 0.1%
3659356 1
< 0.1%
3639618 1
< 0.1%
3637248 1
< 0.1%
3632715 1
< 0.1%
3606027 1
< 0.1%
3567510 1
< 0.1%
3559122 1
< 0.1%
3539434 1
< 0.1%

danceability
Real number (ℝ)

HIGH CORRELATION 

Distinct4160
Distinct (%)71.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54530902
Minimum0.0804
Maximum0.962
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.8 KiB
2023-11-17T00:58:33.905773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0804
5-th percentile0.31281667
Q10.46033333
median0.55005556
Q30.63658971
95-th percentile0.76096538
Maximum0.962
Range0.8816
Interquartile range (IQR)0.17625637

Descriptive statistics

Standard deviation0.13523653
Coefficient of variation (CV)0.24799981
Kurtosis0.076167163
Mean0.54530902
Median Absolute Deviation (MAD)0.088055555
Skewness-0.22992356
Sum3168.2454
Variance0.01828892
MonotonicityNot monotonic
2023-11-17T00:58:34.041919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.478 11
 
0.2%
0.562 10
 
0.2%
0.582 10
 
0.2%
0.484 10
 
0.2%
0.617 10
 
0.2%
0.628 9
 
0.2%
0.587 9
 
0.2%
0.458 9
 
0.2%
0.475 9
 
0.2%
0.621 8
 
0.1%
Other values (4150) 5715
98.4%
ValueCountFrequency (%)
0.0804 1
< 0.1%
0.0811 1
< 0.1%
0.084 2
< 0.1%
0.0866 1
< 0.1%
0.0912 1
< 0.1%
0.096575 1
< 0.1%
0.105 1
< 0.1%
0.109 1
< 0.1%
0.1106 1
< 0.1%
0.114 1
< 0.1%
ValueCountFrequency (%)
0.962 1
< 0.1%
0.928 1
< 0.1%
0.912 1
< 0.1%
0.908 1
< 0.1%
0.9075 1
< 0.1%
0.906 1
< 0.1%
0.903 1
< 0.1%
0.901 1
< 0.1%
0.898190476 1
< 0.1%
0.896 1
< 0.1%

energy
Real number (ℝ)

HIGH CORRELATION 

Distinct4484
Distinct (%)77.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58545841
Minimum0.00198
Maximum0.9995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.8 KiB
2023-11-17T00:58:34.183813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.00198
5-th percentile0.20766794
Q10.43
median0.59848344
Q30.75131262
95-th percentile0.9227
Maximum0.9995
Range0.99752
Interquartile range (IQR)0.32131262

Descriptive statistics

Standard deviation0.21623139
Coefficient of variation (CV)0.3693369
Kurtosis-0.5999758
Mean0.58545841
Median Absolute Deviation (MAD)0.15981677
Skewness-0.26580355
Sum3401.5133
Variance0.046756016
MonotonicityNot monotonic
2023-11-17T00:58:34.343812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.43 10
 
0.2%
0.933 10
 
0.2%
0.587 9
 
0.2%
0.725 9
 
0.2%
0.872 8
 
0.1%
0.647 8
 
0.1%
0.63 7
 
0.1%
0.591 7
 
0.1%
0.658 7
 
0.1%
0.52 7
 
0.1%
Other values (4474) 5728
98.6%
ValueCountFrequency (%)
0.00198 1
< 0.1%
0.00249 2
< 0.1%
0.00459 1
< 0.1%
0.005 1
< 0.1%
0.00835 2
< 0.1%
0.01305 1
< 0.1%
0.0144 1
< 0.1%
0.0156 1
< 0.1%
0.0175 1
< 0.1%
0.01835 1
< 0.1%
ValueCountFrequency (%)
0.9995 1
 
< 0.1%
0.999 1
 
< 0.1%
0.998 1
 
< 0.1%
0.994666667 1
 
< 0.1%
0.9935 1
 
< 0.1%
0.9925 1
 
< 0.1%
0.992 2
< 0.1%
0.991 3
0.1%
0.99 1
 
< 0.1%
0.989 2
< 0.1%

valence
Real number (ℝ)

HIGH CORRELATION 

Distinct4480
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54500934
Minimum0.02785
Maximum0.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.8 KiB
2023-11-17T00:58:34.486186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.02785
5-th percentile0.19545
Q10.40924507
median0.55390685
Q30.68898437
95-th percentile0.871275
Maximum0.98
Range0.95215
Interquartile range (IQR)0.2797393

Descriptive statistics

Standard deviation0.20040944
Coefficient of variation (CV)0.36771743
Kurtosis-0.42190045
Mean0.54500934
Median Absolute Deviation (MAD)0.13988958
Skewness-0.18706766
Sum3166.5043
Variance0.040163942
MonotonicityNot monotonic
2023-11-17T00:58:34.626001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.649 11
 
0.2%
0.471 8
 
0.1%
0.679 8
 
0.1%
0.667 8
 
0.1%
0.96 7
 
0.1%
0.601 7
 
0.1%
0.961 7
 
0.1%
0.488 7
 
0.1%
0.289 7
 
0.1%
0.64 7
 
0.1%
Other values (4470) 5733
98.7%
ValueCountFrequency (%)
0.02785 1
< 0.1%
0.0279 1
< 0.1%
0.03055 1
< 0.1%
0.032675 1
< 0.1%
0.0328 1
< 0.1%
0.03312 1
< 0.1%
0.03505 1
< 0.1%
0.0352 1
< 0.1%
0.03585 1
< 0.1%
0.0364 1
< 0.1%
ValueCountFrequency (%)
0.98 1
 
< 0.1%
0.979 2
< 0.1%
0.978 1
 
< 0.1%
0.976 1
 
< 0.1%
0.972 2
< 0.1%
0.971 1
 
< 0.1%
0.97 2
< 0.1%
0.96975 1
 
< 0.1%
0.969 2
< 0.1%
0.968 3
0.1%

tempo
Real number (ℝ)

Distinct5769
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.36413
Minimum30.946
Maximum206.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.8 KiB
2023-11-17T00:58:34.766614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum30.946
5-th percentile90.508129
Q1109.44504
median119.32445
Q3129.87355
95-th percentile154.30827
Maximum206.68
Range175.734
Interquartile range (IQR)20.428506

Descriptive statistics

Standard deviation19.193444
Coefficient of variation (CV)0.15946149
Kurtosis1.7924309
Mean120.36413
Median Absolute Deviation (MAD)10.210361
Skewness0.57122067
Sum699315.61
Variance368.38831
MonotonicityNot monotonic
2023-11-17T00:58:34.899685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
193.041 3
 
0.1%
141.227 3
 
0.1%
135.903 2
 
< 0.1%
115.869 2
 
< 0.1%
113.325 2
 
< 0.1%
174.311 2
 
< 0.1%
83.444 2
 
< 0.1%
117.126 2
 
< 0.1%
104.153 2
 
< 0.1%
68.434 2
 
< 0.1%
Other values (5759) 5788
99.6%
ValueCountFrequency (%)
30.946 1
< 0.1%
55.807 1
< 0.1%
56.45466667 1
< 0.1%
58.66 1
< 0.1%
59.592 1
< 0.1%
62.825 1
< 0.1%
64.979 1
< 0.1%
66.668 1
< 0.1%
67.117 2
< 0.1%
67.469 1
< 0.1%
ValueCountFrequency (%)
206.68 1
< 0.1%
205.703 1
< 0.1%
203.179 1
< 0.1%
202.409 1
< 0.1%
202.318 1
< 0.1%
201.885 1
< 0.1%
201.712 1
< 0.1%
200.033 1
< 0.1%
199.693 1
< 0.1%
199.04 1
< 0.1%

loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct5653
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.9519694
Minimum-40.147
Maximum1.342
Zeros0
Zeros (%)0.0%
Negative5809
Negative (%)> 99.9%
Memory size90.8 KiB
2023-11-17T00:58:35.041453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-40.147
5-th percentile-17.165432
Q1-12.240722
median-9.5258333
Q3-6.862625
95-th percentile-4.3534698
Maximum1.342
Range41.489
Interquartile range (IQR)5.3780972

Descriptive statistics

Standard deviation4.1803796
Coefficient of variation (CV)-0.42005551
Kurtosis2.9504352
Mean-9.9519694
Median Absolute Deviation (MAD)2.6919333
Skewness-1.1068991
Sum-57820.942
Variance17.475573
MonotonicityNot monotonic
2023-11-17T00:58:35.174333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10.146 3
 
0.1%
-5.541 3
 
0.1%
-4.0355 3
 
0.1%
-6.944 3
 
0.1%
-9.344 3
 
0.1%
-6.979 3
 
0.1%
-4.966 3
 
0.1%
-10.384 3
 
0.1%
-7.305 3
 
0.1%
-6.323 3
 
0.1%
Other values (5643) 5780
99.5%
ValueCountFrequency (%)
-40.147 1
< 0.1%
-35.951 1
< 0.1%
-35.116 1
< 0.1%
-34.366 2
< 0.1%
-33.47625 1
< 0.1%
-33.475 2
< 0.1%
-32.667 1
< 0.1%
-32.652 1
< 0.1%
-32.347 1
< 0.1%
-30.84 1
< 0.1%
ValueCountFrequency (%)
1.342 1
< 0.1%
-0.19 1
< 0.1%
-0.866 1
< 0.1%
-1.092 1
< 0.1%
-1.158 1
< 0.1%
-1.387 1
< 0.1%
-1.714 1
< 0.1%
-1.731 1
< 0.1%
-1.847 1
< 0.1%
-1.987 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size374.5 KiB
1
4769 
0
1041 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5810
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4769
82.1%
0 1041
 
17.9%

Length

2023-11-17T00:58:35.294802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-17T00:58:35.392338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 4769
82.1%
0 1041
 
17.9%

Most occurring characters

ValueCountFrequency (%)
1 4769
82.1%
0 1041
 
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5810
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4769
82.1%
0 1041
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
Common 5810
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4769
82.1%
0 1041
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4769
82.1%
0 1041
 
17.9%

key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5194492
Minimum0
Maximum11
Zeros663
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size90.8 KiB
2023-11-17T00:58:35.478689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.5149706
Coefficient of variation (CV)0.63683358
Kurtosis-1.2305514
Mean5.5194492
Median Absolute Deviation (MAD)3
Skewness-0.1319333
Sum32068
Variance12.355018
MonotonicityNot monotonic
2023-11-17T00:58:35.566793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 925
15.9%
9 736
12.7%
0 663
11.4%
2 637
11.0%
5 528
9.1%
11 460
7.9%
1 398
6.9%
4 385
6.6%
10 359
 
6.2%
6 320
 
5.5%
Other values (2) 399
6.9%
ValueCountFrequency (%)
0 663
11.4%
1 398
6.9%
2 637
11.0%
3 129
 
2.2%
4 385
6.6%
5 528
9.1%
6 320
 
5.5%
7 925
15.9%
8 270
 
4.6%
9 736
12.7%
ValueCountFrequency (%)
11 460
7.9%
10 359
 
6.2%
9 736
12.7%
8 270
 
4.6%
7 925
15.9%
6 320
 
5.5%
5 528
9.1%
4 385
6.6%
3 129
 
2.2%
2 637
11.0%

acousticness
Real number (ℝ)

HIGH CORRELATION 

Distinct5261
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.34386401
Minimum1.01 × 10-6
Maximum0.996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.8 KiB
2023-11-17T00:58:35.689538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.01 × 10-6
5-th percentile0.0017
Q10.081277142
median0.26857404
Q30.578375
95-th percentile0.88934375
Maximum0.996
Range0.99599899
Interquartile range (IQR)0.49709786

Descriptive statistics

Standard deviation0.29506106
Coefficient of variation (CV)0.85807485
Kurtosis-0.9115293
Mean0.34386401
Median Absolute Deviation (MAD)0.22050964
Skewness0.58967187
Sum1997.8499
Variance0.087061031
MonotonicityNot monotonic
2023-11-17T00:58:35.828521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.273 5
 
0.1%
0.367 5
 
0.1%
0.815 5
 
0.1%
0.299 5
 
0.1%
0.988 5
 
0.1%
0.308 4
 
0.1%
0.612 4
 
0.1%
0.2 4
 
0.1%
0.59 4
 
0.1%
0.992 4
 
0.1%
Other values (5251) 5765
99.2%
ValueCountFrequency (%)
1.01 × 10-61
< 0.1%
1.41 × 10-61
< 0.1%
1.59 × 10-61
< 0.1%
3.37 × 10-61
< 0.1%
3.48 × 10-61
< 0.1%
4.12 × 10-61
< 0.1%
4.21 × 10-61
< 0.1%
4.41 × 10-61
< 0.1%
4.45 × 10-61
< 0.1%
6.51 × 10-61
< 0.1%
ValueCountFrequency (%)
0.996 3
0.1%
0.9948125 1
 
< 0.1%
0.994666667 1
 
< 0.1%
0.993 2
< 0.1%
0.992 4
0.1%
0.991333333 1
 
< 0.1%
0.991 2
< 0.1%
0.990070423 1
 
< 0.1%
0.99 2
< 0.1%
0.9895 1
 
< 0.1%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct4931
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1323074
Minimum0
Maximum0.973
Zeros479
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size90.8 KiB
2023-11-17T00:58:35.967197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.63 × 10-5
median0.0086786085
Q30.14299193
95-th percentile0.75233
Maximum0.973
Range0.973
Interquartile range (IQR)0.14289563

Descriptive statistics

Standard deviation0.23373132
Coefficient of variation (CV)1.7665779
Kurtosis2.9483462
Mean0.1323074
Median Absolute Deviation (MAD)0.0086786085
Skewness1.9970166
Sum768.70597
Variance0.054630329
MonotonicityNot monotonic
2023-11-17T00:58:36.108465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 479
 
8.2%
1.18 × 10-57
 
0.1%
1.02 × 10-55
 
0.1%
2.54 × 10-55
 
0.1%
1.84 × 10-54
 
0.1%
2.51 × 10-54
 
0.1%
1.16 × 10-54
 
0.1%
1.3 × 10-54
 
0.1%
0.00225 4
 
0.1%
3.43 × 10-54
 
0.1%
Other values (4921) 5290
91.0%
ValueCountFrequency (%)
0 479
8.2%
6.65 × 10-81
 
< 0.1%
7.23 × 10-81
 
< 0.1%
8.21 × 10-81
 
< 0.1%
8.83 × 10-81
 
< 0.1%
1.09 × 10-71
 
< 0.1%
1.28 × 10-71
 
< 0.1%
1.46 × 10-71
 
< 0.1%
1.54 × 10-71
 
< 0.1%
1.69 × 10-71
 
< 0.1%
ValueCountFrequency (%)
0.973 1
 
< 0.1%
0.963 1
 
< 0.1%
0.962 1
 
< 0.1%
0.957 1
 
< 0.1%
0.9525 1
 
< 0.1%
0.951 3
0.1%
0.949 1
 
< 0.1%
0.9436875 1
 
< 0.1%
0.943 2
< 0.1%
0.942 1
 
< 0.1%

liveness
Real number (ℝ)

Distinct4752
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19564545
Minimum0.0116
Maximum0.973
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.8 KiB
2023-11-17T00:58:36.250641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0116
5-th percentile0.079
Q10.12481791
median0.16986072
Q30.22936382
95-th percentile0.39207944
Maximum0.973
Range0.9614
Interquartile range (IQR)0.10454591

Descriptive statistics

Standard deviation0.1162328
Coefficient of variation (CV)0.59409918
Kurtosis10.361255
Mean0.19564545
Median Absolute Deviation (MAD)0.049860717
Skewness2.6299371
Sum1136.7001
Variance0.013510065
MonotonicityNot monotonic
2023-11-17T00:58:36.406253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.102 16
 
0.3%
0.103 16
 
0.3%
0.109 15
 
0.3%
0.108 14
 
0.2%
0.106 13
 
0.2%
0.115 12
 
0.2%
0.122 12
 
0.2%
0.111 12
 
0.2%
0.13 11
 
0.2%
0.125 11
 
0.2%
Other values (4742) 5678
97.7%
ValueCountFrequency (%)
0.0116 1
< 0.1%
0.0222 1
< 0.1%
0.0224 1
< 0.1%
0.025 1
< 0.1%
0.0258 1
< 0.1%
0.0269 1
< 0.1%
0.0278 1
< 0.1%
0.0302 1
< 0.1%
0.03045 1
< 0.1%
0.0311 2
< 0.1%
ValueCountFrequency (%)
0.973 1
< 0.1%
0.972 1
< 0.1%
0.965 1
< 0.1%
0.964 1
< 0.1%
0.962 1
< 0.1%
0.96 2
< 0.1%
0.951 1
< 0.1%
0.95 1
< 0.1%
0.932 2
< 0.1%
0.922 1
< 0.1%

speechiness
Real number (ℝ)

Distinct4363
Distinct (%)75.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.069731166
Minimum0.0232
Maximum0.958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.8 KiB
2023-11-17T00:58:36.548405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0232
5-th percentile0.030575
Q10.038138988
median0.048655
Q30.072845833
95-th percentile0.171955
Maximum0.958
Range0.9348
Interquartile range (IQR)0.034706845

Descriptive statistics

Standard deviation0.075563982
Coefficient of variation (CV)1.0836472
Kurtosis66.115913
Mean0.069731166
Median Absolute Deviation (MAD)0.013255
Skewness6.9385041
Sum405.13808
Variance0.0057099154
MonotonicityNot monotonic
2023-11-17T00:58:36.686433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0367 12
 
0.2%
0.0372 11
 
0.2%
0.0356 11
 
0.2%
0.0328 11
 
0.2%
0.0314 10
 
0.2%
0.0352 9
 
0.2%
0.0384 9
 
0.2%
0.0331 9
 
0.2%
0.0312 9
 
0.2%
0.0353 9
 
0.2%
Other values (4353) 5710
98.3%
ValueCountFrequency (%)
0.0232 1
 
< 0.1%
0.0236 1
 
< 0.1%
0.024 1
 
< 0.1%
0.0243 1
 
< 0.1%
0.0245 2
< 0.1%
0.0246 2
< 0.1%
0.0252 2
< 0.1%
0.0253 1
 
< 0.1%
0.0256 3
0.1%
0.0257 1
 
< 0.1%
ValueCountFrequency (%)
0.958 1
< 0.1%
0.95 1
< 0.1%
0.948 1
< 0.1%
0.945 2
< 0.1%
0.9416 1
< 0.1%
0.9396 1
< 0.1%
0.936666667 1
< 0.1%
0.924 1
< 0.1%
0.92 1
< 0.1%
0.913 1
< 0.1%

duration_ms
Real number (ℝ)

Distinct5745
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean246401.34
Minimum45707
Maximum1640000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.8 KiB
2023-11-17T00:58:36.822886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum45707
5-th percentile150830.44
Q1197228.25
median234998.92
Q3276208.94
95-th percentile385557.2
Maximum1640000
Range1594293
Interquartile range (IQR)78980.689

Descriptive statistics

Standard deviation83859.416
Coefficient of variation (CV)0.34033669
Kurtosis36.8789
Mean246401.34
Median Absolute Deviation (MAD)39080.171
Skewness3.7307215
Sum1.4315918 × 109
Variance7.0324017 × 109
MonotonicityNot monotonic
2023-11-17T00:58:37.008538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
627330 3
 
0.1%
179800 3
 
0.1%
465713.5 3
 
0.1%
170880 2
 
< 0.1%
389893 2
 
< 0.1%
201067 2
 
< 0.1%
234107 2
 
< 0.1%
319733 2
 
< 0.1%
202133 2
 
< 0.1%
163973 2
 
< 0.1%
Other values (5735) 5787
99.6%
ValueCountFrequency (%)
45707 1
< 0.1%
66333 1
< 0.1%
70587.61538 1
< 0.1%
71400 1
< 0.1%
74033.5 1
< 0.1%
74320 1
< 0.1%
79333 1
< 0.1%
85206.5 1
< 0.1%
87896.75 1
< 0.1%
88822 1
< 0.1%
ValueCountFrequency (%)
1640000 1
< 0.1%
1415707 1
< 0.1%
1260000 1
< 0.1%
1204090 1
< 0.1%
1187042 1
< 0.1%
1171360 1
< 0.1%
1087660 1
< 0.1%
902051.8788 1
< 0.1%
860706.8 1
< 0.1%
853670 1
< 0.1%

popularity
Real number (ℝ)

HIGH CORRELATION 

Distinct2652
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.587142
Minimum0
Maximum81
Zeros33
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size90.8 KiB
2023-11-17T00:58:37.205933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.26
Q132.333333
median40.285714
Q348.5
95-th percentile59.5
Maximum81
Range81
Interquartile range (IQR)16.166667

Descriptive statistics

Standard deviation13.23839
Coefficient of variation (CV)0.33441135
Kurtosis0.5251484
Mean39.587142
Median Absolute Deviation (MAD)8.047619
Skewness-0.50870135
Sum230001.3
Variance175.25497
MonotonicityNot monotonic
2023-11-17T00:58:37.496241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 81
 
1.4%
42 81
 
1.4%
43 71
 
1.2%
39 70
 
1.2%
46 67
 
1.2%
37 66
 
1.1%
44 66
 
1.1%
41 63
 
1.1%
38 62
 
1.1%
35 62
 
1.1%
Other values (2642) 5121
88.1%
ValueCountFrequency (%)
0 33
0.6%
0.2 1
 
< 0.1%
0.301724138 1
 
< 0.1%
0.333333333 1
 
< 0.1%
0.384615385 1
 
< 0.1%
0.410958904 1
 
< 0.1%
0.538461538 1
 
< 0.1%
0.571428571 1
 
< 0.1%
0.6 1
 
< 0.1%
0.823529412 1
 
< 0.1%
ValueCountFrequency (%)
81 1
< 0.1%
77.50704225 1
< 0.1%
77.03846154 1
< 0.1%
77 1
< 0.1%
76.83333333 1
< 0.1%
76 1
< 0.1%
74.8 1
< 0.1%
74 2
< 0.1%
73.5 2
< 0.1%
73.24193548 1
< 0.1%

count
Real number (ℝ)

Distinct300
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.50241
Minimum1
Maximum1369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.8 KiB
2023-11-17T00:58:37.773327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median10
Q331
95-th percentile137
Maximum1369
Range1368
Interquartile range (IQR)27

Descriptive statistics

Standard deviation77.300072
Coefficient of variation (CV)2.3072989
Kurtosis81.728731
Mean33.50241
Median Absolute Deviation (MAD)8
Skewness7.4876933
Sum194649
Variance5975.3012
MonotonicityDecreasing
2023-11-17T00:58:38.513235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1097
18.9%
4 581
 
10.0%
6 384
 
6.6%
8 274
 
4.7%
10 239
 
4.1%
12 200
 
3.4%
14 159
 
2.7%
18 148
 
2.5%
16 147
 
2.5%
1 138
 
2.4%
Other values (290) 2443
42.0%
ValueCountFrequency (%)
1 138
 
2.4%
2 1097
18.9%
3 78
 
1.3%
4 581
10.0%
5 76
 
1.3%
6 384
 
6.6%
7 67
 
1.2%
8 274
 
4.7%
9 44
 
0.8%
10 239
 
4.1%
ValueCountFrequency (%)
1369 1
< 0.1%
1207 1
< 0.1%
1104 1
< 0.1%
1095 1
< 0.1%
1092 1
< 0.1%
1035 1
< 0.1%
994 1
< 0.1%
990 1
< 0.1%
938 1
< 0.1%
864 1
< 0.1%

Interactions

2023-11-17T00:58:30.494272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:01.913114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:04.174194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:05.981920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:08.836227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:11.665171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:14.005418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:15.880223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:17.551141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:19.312729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:21.305784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:23.143214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:26.011440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:28.187828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:30.628006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:02.134641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:04.321238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:06.164864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:08.972425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:11.863695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:14.145392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:16.003810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:17.680030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:19.456035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:21.440966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:23.319046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:26.225222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:28.321427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:30.748572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:02.370933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:04.450527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:06.287253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:09.097427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:12.103767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:14.458757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:16.119348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:17.801777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:19.568943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:21.558659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:23.562715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:26.483294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:28.451930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:30.867103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:02.595607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:04.567593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:06.413038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:09.243703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:12.369002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:14.574753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:16.230886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:17.930475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:19.685097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:21.677642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:23.754121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:26.712269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:28.582863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:30.992039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:02.821785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:04.697573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:06.539688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:09.423641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:12.647840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:14.689472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:16.351635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:18.049178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:19.801420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:21.788530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:23.994695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:26.902735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:28.724298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:31.127964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:03.048577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:04.825924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:07.683586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:09.651314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:12.867994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:14.812453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:16.478838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:18.180186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:19.918537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:21.909285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:24.185975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:27.037676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:28.854191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:31.266081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:03.166188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:04.939500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:07.811298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:09.844472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:12.986143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:14.930470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:16.589825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:18.294947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:20.036314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:22.031047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:24.412406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:27.165207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:28.969869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:31.405910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:03.288017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:05.073827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:07.930920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:10.085216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:13.109349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:15.043793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:16.702281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:18.418037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:20.152098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:22.144058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:24.602280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:27.282994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:29.093254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:31.521662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:03.406591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:05.191826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:08.058685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:10.325102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:13.231863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:15.158672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:16.817363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:18.551307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:20.270590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:22.263838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:24.793566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:27.412270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:29.227705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:31.648663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:03.533925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:05.328759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:08.181347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:10.550325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:13.355251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:15.272431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:16.944855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:18.676541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:20.668230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:22.389850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:25.025109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:27.535387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:29.356344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:31.768934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:03.659747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:05.448334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:08.302280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:10.794020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:13.474134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:15.385046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:17.062771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:18.793768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:20.783290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:22.513265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:25.272327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:27.663320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:29.486477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:31.892617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:03.785490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:05.576274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:08.430289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:11.020223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:13.594634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:15.503927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:17.176314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:18.911236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:20.900353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:22.632761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:25.458091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:27.783125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:29.930169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:32.027820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:03.915994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:05.719790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:08.566079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:11.274559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:13.733383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:15.631017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:17.304209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:19.047415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:21.058079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:22.761959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:25.697435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:27.916712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:30.077991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:32.167089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:04.051636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:05.854316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:08.697355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:11.528932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:13.879355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:15.756736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:17.433777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:19.188607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:21.186859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:22.987233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:25.853315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:28.052464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T00:58:30.223606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-11-17T00:58:38.769747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
artist_iddanceabilityenergyvalencetempoloudnesskeyacousticnessinstrumentalnesslivenessspeechinessduration_mspopularitycountmode
artist_id1.0000.0250.059-0.079-0.0020.132-0.019-0.070-0.055-0.0190.038-0.0280.198-0.0280.050
danceability0.0251.0000.0070.564-0.1230.0720.012-0.026-0.188-0.1840.088-0.0140.169-0.0410.147
energy0.0590.0071.0000.1940.3310.7840.045-0.793-0.1090.1980.2770.0150.355-0.1070.055
valence-0.0790.5640.1941.0000.0930.0730.009-0.061-0.156-0.0090.058-0.219-0.129-0.0190.067
tempo-0.002-0.1230.3310.0931.0000.2510.015-0.291-0.0440.0660.101-0.0590.1020.0010.057
loudness0.1320.0720.7840.0730.2511.0000.035-0.615-0.3110.1300.204-0.0700.521-0.0900.085
key-0.0190.0120.0450.0090.0150.0351.000-0.060-0.017-0.0080.019-0.0070.032-0.0320.150
acousticness-0.070-0.026-0.793-0.061-0.291-0.615-0.0601.0000.075-0.083-0.179-0.121-0.3970.1440.081
instrumentalness-0.055-0.188-0.109-0.156-0.044-0.311-0.0170.0751.000-0.019-0.0270.243-0.2370.2000.090
liveness-0.019-0.1840.198-0.0090.0660.130-0.008-0.083-0.0191.0000.178-0.067-0.0800.2130.092
speechiness0.0380.0880.2770.0580.1010.2040.019-0.179-0.0270.1781.000-0.0340.0250.0810.072
duration_ms-0.028-0.0140.015-0.219-0.059-0.070-0.007-0.1210.243-0.067-0.0341.0000.123-0.0110.124
popularity0.1980.1690.355-0.1290.1020.5210.032-0.397-0.237-0.0800.0250.1231.000-0.1380.109
count-0.028-0.041-0.107-0.0190.001-0.090-0.0320.1440.2000.2130.081-0.011-0.1381.0000.064
mode0.0500.1470.0550.0670.0570.0850.1500.0810.0900.0920.0720.1240.1090.0641.000

Missing values

2023-11-17T00:58:32.368213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-17T00:58:32.611394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

artist_nameartist_iddanceabilityenergyvalencetempoloudnessmodekeyacousticnessinstrumentalnesslivenessspeechinessduration_mspopularitycount
0Frank Sinatra7925070.3844780.2380170.364288110.181698-14.271141150.7356480.0208550.2321060.049614189179.925526.0043831369
1Vladimir Horowitz1191070.3432100.1188440.22595194.900679-23.193418110.9900700.8795080.1838120.043360266541.12513.5923781207
2Johnny Cash8168900.6198030.4493810.680662115.037747-11.5931041100.6856370.0226470.2422430.098216162279.267226.6141301104
3Billie Holiday790160.5726370.2013680.498934109.912172-13.225966150.9084990.0130640.2177270.062432185131.453015.6210051095
4Bob Dylan669150.5125980.4779320.551934126.160149-11.184330170.5625670.0342110.3089780.064535256713.420330.8608061092
5The Rolling Stones8944650.5244460.7199150.655332123.764717-7.830265100.2937880.1761370.2684430.051440229705.962334.5739131035
6The Beach Boys418740.5029450.5321310.633957125.992036-9.925742190.3984880.1153630.1911890.043971148845.094627.957746994
7Elvis Presley1802280.4958430.4263080.621249111.489453-12.893730100.7414120.0536230.2473460.058278156211.035433.391919990
8Wolfgang Amadeus Mozart263500.3533460.1378690.330529108.604340-20.174257170.9615720.5088810.1888280.068485329702.92438.936034938
9Miles Davis4238290.4602210.3082290.417860113.550382-14.526619000.6557110.2050730.2197620.054571404023.445622.700231864
artist_nameartist_iddanceabilityenergyvalencetempoloudnessmodekeyacousticnessinstrumentalnesslivenessspeechinessduration_mspopularitycount
5844Jonn Hart30437870.6860.5410.23796.965-6.643040.0971000.0000000.42300.0356189613.049.01
5845Hiatus Kaiyote30456200.5520.5310.32491.573-6.767180.6560000.0000520.11000.1250275320.057.01
5846Gawvi30995730.6830.6300.452159.927-5.146110.3200000.0000000.22200.0587203325.060.01
5847Adrian Marcel31204580.8490.5340.599102.014-6.365140.0472000.0000000.25000.0699237653.059.01
5848Karen Harding33086650.7010.6670.675119.975-6.177170.0121000.0000070.14800.0361213135.067.01
5849Natalie La Rose33595190.8300.5200.735104.990-8.714100.0007920.0000130.06560.0376189907.064.01
5850Sarah Ross33815660.7210.9440.62685.002-5.982180.0130000.0000000.32000.1590262760.052.01
5851Rotimi34102500.6370.5010.431103.993-6.148000.2290000.0000590.09900.1870185461.071.01
5852Jillian Jacqueline34559450.5470.6720.283155.791-5.0231110.3040000.0000000.09960.0496213133.058.01
5853Jaira Burns36396180.5660.7690.385170.036-4.342170.0183000.0000000.10800.0872191100.074.01